library("FRESA.CAD")
library(survival)
library(readxl)
library(igraph)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
load("./TADPOLE_BSWIMS_Results.RData")
pander::pander(table(TADPOLE_Conv_TRAIN$status))
| 0 | 1 |
|---|---|
| 261 | 133 |
pander::pander(table(TADPOLE_Conv_TEST$status))
| 0 | 1 |
|---|---|
| 112 | 58 |
par(op)
cvBESSRaw <- randomCV(TADPOLECrossMRI,
Surv(TimeToEvent,status)~.,
fittingFunction= BESS,
trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
)
……….10 Tested: 552 Avg. Selected: 51 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.173326 ……….20 Tested: 562 Avg. Selected: 51 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.11624 ……….30 Tested: 564 Avg. Selected: 51 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.117324 ……….40 Tested: 564 Avg. Selected: 51 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.115381 ……….50 Tested: 564 Avg. Selected: 51 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.115094
pander::pander(cbind(cvBESSRaw$featureFrequency[cvBESSRaw$featureFrequency>20]))
| FAQ | 50 |
| RAVLT_immediate | 48 |
| ADAS13 | 45 |
| RD_ST50TS | 42 |
| RD_ST34TA | 41 |
| RD_ST47TS | 41 |
| ABETA | 38 |
| RD_ST45TA | 34 |
| M_ST66SV | 31 |
| RD_ST31TA | 31 |
| RD_ST32TS | 30 |
| RD_ST58CV | 30 |
| RD_ST12SV | 29 |
| RD_ST129TS | 27 |
| RD_ST51SA | 27 |
| APOE4 | 26 |
| M_ST62SA | 26 |
| PTAU | 26 |
| RD_ST25TS | 26 |
| RD_ST35TS | 26 |
| M_ST53SV | 25 |
| RD_ST44CV | 25 |
| RD_ST24SA | 24 |
| RD_ST60TS | 22 |
| RD_ST15TA | 21 |
prBin <- predictionStats_binary(cvBESSRaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to AD Conversion")
survmtest <- cvBESSRaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]
prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:BIC: MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.832 | 0.798 | 0.867 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 150 | 106 | 256 |
| Test - | 41 | 267 | 308 |
| Total | 191 | 373 | 564 |
par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:BIC")
[1] 0.3386525 [1] 0.3386525 1.0000000
[1]
0.7771272 0.8672770 0.3942505 3.4912148 49.5056709 101.3358977 [7]
0.0000000 1.0000000
pander::pander(riskAnalysis$c.index)
C Index: 0.818
Dxy: 0.636
S.D.: 0.0278
n: 564
missing: 0
uncensored: 191
Relevant Pairs: 142910
Concordant: 116890
Uncertain: 174472
cstatCI:
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.818 | 0.817 | 0.788 | 0.844 |
pander::pander(riskAnalysis$ROCAnalysis$aucs)
| est | lower | upper |
|---|---|---|
| 0.831 | 0.796 | 0.866 |
pander::pander(riskAnalysis$cenAUC)
0.853
pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)
accci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.785 | 0.752 | 0.817 |
senci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.634 | 0.561 | 0.7 |
speci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.864 | 0.828 | 0.898 |
aucci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.749 | 0.709 | 0.786 |
berci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.251 | 0.214 | 0.291 |
preci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.703 | 0.633 | 0.771 |
F1ci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.667 | 0.607 | 0.719 |
pander::pander(riskAnalysis$surdif)
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 334 | 49 | 133.7 | 53.6913 | 183.7155 |
| class=1 | 58 | 21 | 20.3 | 0.0271 | 0.0306 |
| class=2 | 172 | 121 | 37.0 | 190.6823 | 244.3976 |
cvBESSERaw <- randomCV(TADPOLECrossMRI,
Surv(TimeToEvent,status)~.,
fittingFunction= BESS_EBIC,
trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
)
……….10 Tested: 552 Avg. Selected: 51 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.173326 ……….20 Tested: 562 Avg. Selected: 51 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.11624 ……….30 Tested: 564 Avg. Selected: 51 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.117324 ……….40 Tested: 564 Avg. Selected: 51 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.115381 ……….50 Tested: 564 Avg. Selected: 51 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.115094
pander::pander(cbind(cvBESSERaw$featureFrequency[cvBESSERaw$featureFrequency>20]))
| FAQ | 50 |
| RAVLT_immediate | 48 |
| ADAS13 | 45 |
| RD_ST50TS | 42 |
| RD_ST34TA | 41 |
| RD_ST47TS | 41 |
| ABETA | 38 |
| RD_ST45TA | 34 |
| M_ST66SV | 31 |
| RD_ST31TA | 31 |
| RD_ST32TS | 30 |
| RD_ST58CV | 30 |
| RD_ST12SV | 29 |
| RD_ST129TS | 27 |
| RD_ST51SA | 27 |
| APOE4 | 26 |
| M_ST62SA | 26 |
| PTAU | 26 |
| RD_ST25TS | 26 |
| RD_ST35TS | 26 |
| M_ST53SV | 25 |
| RD_ST44CV | 25 |
| RD_ST24SA | 24 |
| RD_ST60TS | 22 |
| RD_ST15TA | 21 |
prBin <- predictionStats_binary(cvBESSERaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to AD Conversion")
survmtest <- cvBESSERaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]
prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:EBIC: MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.832 | 0.798 | 0.867 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 150 | 106 | 256 |
| Test - | 41 | 267 | 308 |
| Total | 191 | 373 | 564 |
par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:EBIC")
[1] 0.3386525 [1] 0.3386525 1.0000000
[1]
0.7771272 0.8672770 0.3942505 3.4912148 49.5056709 101.3358977 [7]
0.0000000 1.0000000
pander::pander(riskAnalysis$c.index)
C Index: 0.818
Dxy: 0.636
S.D.: 0.0278
n: 564
missing: 0
uncensored: 191
Relevant Pairs: 142910
Concordant: 116890
Uncertain: 174472
cstatCI:
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.818 | 0.817 | 0.79 | 0.842 |
pander::pander(riskAnalysis$ROCAnalysis$aucs)
| est | lower | upper |
|---|---|---|
| 0.831 | 0.796 | 0.866 |
pander::pander(riskAnalysis$cenAUC)
0.853
pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)
accci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.785 | 0.75 | 0.817 |
senci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.633 | 0.564 | 0.7 |
speci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.864 | 0.829 | 0.899 |
aucci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.748 | 0.709 | 0.785 |
berci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.252 | 0.215 | 0.291 |
preci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.706 | 0.635 | 0.77 |
F1ci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.668 | 0.608 | 0.721 |
pander::pander(riskAnalysis$surdif)
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 334 | 49 | 133.7 | 53.6913 | 183.7155 |
| class=1 | 58 | 21 | 20.3 | 0.0271 | 0.0306 |
| class=2 | 172 | 121 | 37.0 | 190.6823 | 244.3976 |
cvBESSGSERaw <- randomCV(TADPOLECrossMRI,
Surv(TimeToEvent,status)~.,
fittingFunction= BESS_GSECTION,
trainSampleSets= cvBSWiMSRaw$trainSamplesSets,
)
.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 552 Avg. Selected: 50 Min Tests: 1 Max Tests: 10 Mean Tests: 5.036232 . MAD: 1.152137 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:32 s.split:39 s.right:44-th iteration s.left:39 s.split:42 s.right:44-th iteration s.left:42 s.split:43 s.right:44.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:32 s.split:39 s.right:44-th iteration s.left:39 s.split:42 s.right:44-th iteration s.left:42 s.split:43 s.right:44 Tested: 562 Avg. Selected: 49.3 Min Tests: 1 Max Tests: 17 Mean Tests: 9.893238 . MAD: 1.108346 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.53333 Min Tests: 3 Max Tests: 25 Mean Tests: 14.78723 . MAD: 1.12239 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.65 Min Tests: 4 Max Tests: 32 Mean Tests: 19.71631 . MAD: 1.119568 .1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51.1-th iteration s.left:1 s.split:32 s.right:51-th iteration s.left:32 s.split:44 s.right:51-th iteration s.left:44 s.split:48 s.right:51-th iteration s.left:48 s.split:50 s.right:51 Tested: 564 Avg. Selected: 49.72 Min Tests: 6 Max Tests: 39 Mean Tests: 24.64539 . MAD: 1.113176
pander::pander(cbind(cvBESSGSERaw$featureFrequency[cvBESSGSERaw$featureFrequency>20]))
| FAQ | 50 |
| RAVLT_immediate | 50 |
| ADAS13 | 39 |
| M_ST13TA | 38 |
| M_ST24CV | 37 |
| M_ST29SV | 37 |
| RD_ST34TA | 37 |
| RD_ST47TS | 37 |
| ABETA | 34 |
| RD_ST50TS | 32 |
| RD_ST12SV | 31 |
| M_ST66SV | 30 |
| RAVLT_learning | 30 |
| M_ST62SA | 29 |
| RD_ST129TS | 29 |
| WholeBrain | 28 |
| RD_ST44CV | 27 |
| RD_ST45TA | 26 |
| M_ST129CV | 25 |
| APOE4 | 24 |
| PTAU | 24 |
| RD_ST31TA | 24 |
| RD_ST32TS | 24 |
| RD_ST51SA | 24 |
| RD_ST35TS | 23 |
| TAU | 23 |
| M_ST26TA | 22 |
| M_ST56CV | 22 |
| RD_ST24SA | 22 |
| RD_ST46TA | 22 |
| RD_ST58CV | 22 |
| M_ST31TA | 21 |
prBin <- predictionStats_binary(cvBESSGSERaw$survMedianTrain[,c(2,3)],"TRAIN: MCI to AD Conversion")
survmtest <- cvBESSGSERaw$survMedianTest
survmtest <- survmtest[complete.cases(survmtest),]
prBin <- predictionStats_binary(survmtest[,c(2,3)],"BESS:GS: MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.838 | 0.804 | 0.872 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 156 | 104 | 260 |
| Test - | 35 | 269 | 304 |
| Total | 191 | 373 | 564 |
par(op)
ho <- mean(survmtest$Outcome)
timeInterval <- mean(survmtest[survmtest$Outcome==0,"Times"])
pgzero <- ppoisGzero(survmtest$LinearPredictorsMedian,ho)
rsdata <- cbind(survmtest$Outcome,pgzero,survmtest$Times)
riskAnalysis <- RRPlot(rsdata,riskTimeInterval=timeInterval,title="BESS:GS")
[1] 0.3386525 [1] 0.3386525 1.0000000
[1]
0.7753446 0.8666518 0.3942505 3.4912148 49.7388732 96.6557288 0.0000000
[8] 1.0000000
pander::pander(riskAnalysis$c.index)
C Index: 0.819
Dxy: 0.639
S.D.: 0.0264
n: 564
missing: 0
uncensored: 191
Relevant Pairs: 142910
Concordant: 117108
Uncertain: 174472
cstatCI:
| mean.C Index | median | lower | upper |
|---|---|---|---|
| 0.819 | 0.82 | 0.794 | 0.847 |
pander::pander(riskAnalysis$ROCAnalysis$aucs)
| est | lower | upper |
|---|---|---|
| 0.837 | 0.803 | 0.872 |
pander::pander(riskAnalysis$cenAUC)
0.858
pander::pander(riskAnalysis$ROCAnalysis$ClassMetrics)
accci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.787 | 0.755 | 0.819 |
senci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.628 | 0.562 | 0.698 |
speci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.869 | 0.834 | 0.902 |
aucci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.749 | 0.712 | 0.786 |
berci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.251 | 0.214 | 0.288 |
preci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.711 | 0.637 | 0.779 |
F1ci:
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.667 | 0.608 | 0.72 |
pander::pander(riskAnalysis$surdif)
| N | Observed | Expected | (O-E)^2/E | (O-E)^2/V | |
|---|---|---|---|---|---|
| class=0 | 327 | 41 | 131.3 | 62.12 | 203.95 |
| class=1 | 68 | 30 | 23.6 | 1.71 | 1.98 |
| class=2 | 169 | 120 | 36.0 | 195.52 | 248.28 |
bConvmBess <- BESS(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
pander::pander(bConvmBess$selectedfeatures)
ADAS13, MMSE, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, ABETA, PTAU, ST2SV, M_ST25TA, M_ST32TA, M_ST54TA, M_ST34TS, M_ST47TS, M_ST49TS, M_ST55TS, M_ST14SA, M_ST25SA, M_ST38SA, M_ST44SA, M_ST45SA, M_ST56SA, M_ST23CV, M_ST24CV, M_ST36CV, M_ST50CV, M_ST52CV, M_ST11SV, M_ST16SV, M_ST29SV, M_ST65SV, M_ST66SV, RD_ST13TA, RD_ST34TA, RD_ST36TA, RD_ST45TA, RD_ST47TA, RD_ST48TA, RD_ST51TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST23TS, RD_ST34TS, RD_ST36TS, RD_ST47TS, RD_ST50TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST31SA, RD_ST34SA, RD_ST60SA, RD_ST129CV, RD_ST40CV, RD_ST43CV, RD_ST44CV, RD_ST47CV, RD_ST48CV, RD_ST49CV, RD_ST52CV, RD_ST56CV, RD_ST12SV, RD_ST29SV and RD_ST61SV
ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBess,TADPOLE_Conv_TEST) - cval
boxplot(ptestl~TADPOLE_Conv_TEST$status)
ptestr <- exp(ptestl)
predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
prSurv <- predictionStats_survival(predsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
| median | lower | upper |
|---|---|---|
| 0.847 | 0.799 | 0.887 |
pander::pander(prSurv$CILp)
| median | lower | upper |
|---|---|---|
| 0.854 | 0.797 | 0.907 |
pander::pander(prSurv$spearmanCI)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.449 | 0.218 | 0.628 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.854 | 0.797 | 0.912 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 48 | 35 | 83 |
| Test - | 10 | 77 | 87 |
| Total | 58 | 112 | 170 |
par(op)
bConvmBessE <- BESS_EBIC(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
pander::pander(bConvmBessE$selectedfeatures)
ADAS13, MMSE, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, ABETA, PTAU, ST2SV, M_ST25TA, M_ST32TA, M_ST54TA, M_ST34TS, M_ST47TS, M_ST49TS, M_ST55TS, M_ST14SA, M_ST25SA, M_ST38SA, M_ST44SA, M_ST45SA, M_ST56SA, M_ST23CV, M_ST24CV, M_ST36CV, M_ST50CV, M_ST52CV, M_ST11SV, M_ST16SV, M_ST29SV, M_ST65SV, M_ST66SV, RD_ST13TA, RD_ST34TA, RD_ST36TA, RD_ST45TA, RD_ST47TA, RD_ST48TA, RD_ST51TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST23TS, RD_ST34TS, RD_ST36TS, RD_ST47TS, RD_ST50TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST31SA, RD_ST34SA, RD_ST60SA, RD_ST129CV, RD_ST40CV, RD_ST43CV, RD_ST44CV, RD_ST47CV, RD_ST48CV, RD_ST49CV, RD_ST52CV, RD_ST56CV, RD_ST12SV, RD_ST29SV and RD_ST61SV
ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessE,TADPOLE_Conv_TEST) - cval
boxplot(ptestl~TADPOLE_Conv_TEST$status)
ptestr <- exp(ptestl)
predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
prSurv <- predictionStats_survival(predsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
| median | lower | upper |
|---|---|---|
| 0.863 | 0.816 | 0.903 |
pander::pander(prSurv$CILp)
| median | lower | upper |
|---|---|---|
| 0.882 | 0.829 | 0.931 |
pander::pander(prSurv$spearmanCI)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.48 | 0.265 | 0.659 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.882 | 0.832 | 0.931 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 50 | 26 | 76 |
| Test - | 8 | 86 | 94 |
| Total | 58 | 112 | 170 |
par(op)
bConvmBessGS <- BESS_GSECTION(Surv(TimeToEvent,status)~.,TADPOLE_Conv_TRAIN)
1-th iteration s.left:1 s.split:41 s.right:66-th iteration s.left:41 s.split:56 s.right:66-th iteration s.left:56 s.split:62 s.right:66-th iteration s.left:62 s.split:64 s.right:66-th iteration s.left:64 s.split:65 s.right:66
pander::pander(bConvmBessGS$selectedfeatures)
ADAS11, ADAS13, RAVLT_immediate, RAVLT_learning, FAQ, APOE4, PTAU, ST68SV, M_ST15TA, M_ST26TA, M_ST32TA, M_ST38TA, M_ST44TA, M_ST45TA, M_ST50TA, M_ST52TA, M_ST57TA, M_ST59TA, M_ST23TS, M_ST60TS, M_ST35SA, M_ST48SA, M_ST24CV, M_ST129CV, M_ST36CV, M_ST38CV, M_ST44CV, M_ST52CV, M_ST56CV, M_ST58CV, M_ST11SV, M_ST16SV, RD_ST13TA, RD_ST34TA, RD_ST38TA, RD_ST45TA, RD_ST46TA, RD_ST47TA, RD_ST49TA, RD_ST50TA, RD_ST58TA, RD_ST60TA, RD_ST15TS, RD_ST26TS, RD_ST35TS, RD_ST46TS, RD_ST47TS, RD_ST59TS, RD_ST60TS, RD_ST23SA, RD_ST24SA, RD_ST25SA, RD_ST34SA, RD_ST13CV, RD_ST32CV, RD_ST44CV, RD_ST49CV, RD_ST62CV, RD_ST11SV, RD_ST12SV, RD_ST16SV, RD_ST30SV, RD_ST42SV, RD_ST61SV and RD_ST65SV
ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST)
cval <- mean(ptestl)
ptestl <- predict(bConvmBessGS,TADPOLE_Conv_TEST) - cval
boxplot(ptestl~TADPOLE_Conv_TEST$status)
ptestr <- exp(ptestl)
predsurv <- cbind(TADPOLE_Conv_TEST$TimeToEvent,
TADPOLE_Conv_TEST$status,
ptestl,
ptestr)
prSurv <- predictionStats_survival(predsurv,"MCI to AD Conversion")
pander::pander(prSurv$CIRisk)
| median | lower | upper |
|---|---|---|
| 0.788 | 0.731 | 0.84 |
pander::pander(prSurv$CILp)
| median | lower | upper |
|---|---|---|
| 0.779 | 0.703 | 0.851 |
pander::pander(prSurv$spearmanCI)
| 50% | 2.5% | 97.5% |
|---|---|---|
| 0.409 | 0.149 | 0.612 |
prBin <- predictionStats_binary(cbind(TADPOLE_Conv_TEST$status,ptestl),"MCI to AD Conversion")
pander::pander(prBin$aucs)
| est | lower | upper |
|---|---|---|
| 0.777 | 0.706 | 0.848 |
pander::pander(prBin$CM.analysis$tab)
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 44 | 40 | 84 |
| Test - | 14 | 72 | 86 |
| Total | 58 | 112 | 170 |
par(op)
save.image("./TADPOLE_BESS_Results.RData")